Invariant neural subspaces maintained by feedback modulation

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Abstract

Sensory systems reliably process incoming stimuli in spite of changes in context. Most recent models accredit this context invariance to an extraction of increasingly complex sensory features in hierarchical feedforward networks. Here, we study how context-invariant representations can be established by feedback rather than feedforward processing. We show that feedforward neural networks modulated by feedback can dynamically generate invariant sensory representations. The required feedback can be implemented as a slow and spatially diffuse gain modulation. The invariance is not present on the level of individual neurons, but emerges only on the population level. Mechanistically, the feedback modulation dynamically reorients the manifold of neural activity and thereby maintains an invariant neural subspace in spite of contextual variations. Our results highlight the importance of population-level analyses for understanding the role of feedback in flexible sensory processing.

Data availability

The current manuscript is a computational study, so no data have been generated for this manuscript. Modelling code will be available under https://github.com/sprekelerlab/feedback_modulation_Naumann21 upon publication.

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Laura B Naumann

Technical University of Berlin, Berlin, Germany

For correspondence
laurabella.naumann@bccn-berlin.de

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-7919-7349
Joram Keijser

Technical University of Berlin, Berlin, Germany

Competing interests
The authors declare that no competing interests exist.
Henning Sprekeler

Technical University of Berlin, Berlin, Germany

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-0690-3553

Funding

No external funding was received for this work.

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This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.